Sentiment Analysis (SA) which is sometimes referred to as Opinion Mining, is a subarea of NLP [1]}, [2]}, [3]}, [4]}, [5]}. SA studies people's opinions, sentiments, and attitudes toward entities in a text. SA aims to classify words into two categories: `good' and `bad', and then measure an overall good/bad score in a text. SA can be described as a classification process. The goal of SA is to achieve the following tasks: i) find opinions, ii) identify the sentiments they express, and iii) classify their sentiment. Conceptually, this process is designed in four steps. The first step in SA is the Sentiment Identification. The input is a text from any domain. The goal is to determine whether the content of the text is subjective or objective. The second step in SA process is Feature-Based Classification. The feature-based classification task treats the text either as bag-of-words (BOWs), or as a string which retains the sequence of words in the document. The third step in SA process is the task of Sentiment Classification, which includes machine learning classification approach or lexicon-based approach. Either of these techniques can be applied to one of the three classification levels: document-level, sentence-level, or aspect-level. In our approach, we employ lexicon-based classification applied to document-level (i.e., comments). We decided on following this approach because it involves calculating orientation for a bug-fixing comment from the semantic orientation of words or phrases in the comment. Hence, lexicon-based classification relies on a sentiment lexicon, a collection of known and precompiled sentiment terms. It depends on finding the opinion lexicon by using corpus-based approach. The corpus-based approach begins with a seed list of opinion words, and then finds other opinion words in a large corpus to help in finding opinion words with context specific orientation. The fourth step in SA process is the Sentiment Polarity. The sentiment polarity of a word can be identified by computing the occurrence frequency of the word in a large annotated corpus of texts [6]}. If the word occurs more frequently among positive texts, then its polarity is positive. If it occurs more frequently among negative texts, then its polarity is negative. If it has equal frequencies, then it is a neutral word. Our focus is to find the sentiments polarity [7]} of terms in bug comments as described in the forth step. To achieve this goal, we used NLP python module TextBlob Sentiment Classifier [8]} for processing text. TextBlob takes the text and returns a blob with sentences splitted. Then, built-in sentiment analyzer performs the following functionalities such as extracting terms using Naive Bayes [9]} and calculating sentiment for each term using a lexicon-based score. The lexicon-based score is obtained by using averaging technique [10]}, [11]}, in which case each term returns sentiment polarity score. The polarity score is determined within ranges of \([-1.0, 1.0]\) , where \(1.0\)  is highly positive and -\(1.0\)  is highly negative. An example to a positive comment is:“Try using String class [...] it works fine [...]”.
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